class_docstr: Given wrapped example, e.g. A fun movie ! it is <mask> and <mask> .
  The input tokens is:  A fun movie ! it is <extra_id_0> and <extra_id_1> . </s>
  The decoder tokens is: <pad> <extra_id_0> <extra_id_1> </s>
  The expected output is <extra_id_0> good interesting </s>
code: def num_special_tokens_to_add(self):
      if not hasattr(self, "_num_specials"):
          self._num_specials = (
              self.tokenizer.num_special_tokens_to_add()
          )
      return self._num_specials
  
imports: from json import decoder
  from transformers.tokenization_utils import PreTrainedTokenizer
  from openprompt.plms.utils import TokenizerWrapper
  from typing import List, Dict, Optional
  from collections import defaultdict
  from openprompt.utils.logging import logger
label_desc: Use image node_id 4 for calling the T5TokenizerWrapper obj's underlying member method code with example usage: obj.num_special_tokens_to_add() and returns: self